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Hack-R's answer (the first one) to this question:

How to actually plot a sample tree from randomForest::getTree()?

shows a plot for a single binary decision tree in a random forest. I was wondering what the "p<0.001" represents?

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It's because the random forest algorithm being used is based on a conditional inference framework (rather than Breiman's original algorithm). It essentially determines whether a split should be carried out by using a p-value calculated from a permutation test, corrected for multiple comparisons. The main benefit of this approach over Breiman's is that conditional inference forests yield unbiased variable importance metrics. It's available in R using the party package, and you can read more about it here.

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